Zero-Shot Classification
GLiNER2
Safetensors
English
Russian
extractor
safety
pii
ai-security
zero-shot
text-classification
span-categorization
token-classification
guardrails
Instructions to use hivetrace/gliner-guard-uniencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER2
How to use hivetrace/gliner-guard-uniencoder with GLiNER2:
from gliner2 import GLiNER2 model = GLiNER2.from_pretrained("hivetrace/gliner-guard-uniencoder") # Extract entities text = "Apple CEO Tim Cook announced iPhone 15 in Cupertino yesterday." result = extractor.extract_entities(text, ["company", "person", "product", "location"]) print(result) - Notebooks
- Google Colab
- Kaggle
File size: 354 Bytes
ebcf82a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"counting_layer": "count_lstm_v2",
"cross_fuser_heads": 0,
"cross_fuser_layers": 1,
"encoder_mode": "uni",
"max_len": null,
"max_width": 12,
"model_name": "bogdanminko/mmBERT-small",
"model_type": "extractor",
"schema_model_name": null,
"schema_projection_dim": null,
"token_pooling": "first",
"transformers_version": "5.3.0"
}
|